改进FCOS网络的海洋鱼类目标检测

鱼类的探索与保护是保持海洋生态环境平衡的重要一环,然而水下环境复杂,受光照、水质以及遮挡物的影响,造成水下捕捉鱼类图像成像模糊识别困难,制约水下鱼类目标的检测速度以及检测精度.针对以上问题,提出了一种基于改进FCOS的海洋鱼类识别模型.首先,该模型以一阶段算法FCOS为基本架构,使用轻量级的Mobile Netv2作为骨干网络,既保证检测准确度,还可以提高检测;其次,引入自适应空间特征融合(adaptively spatial feature fusion, ASFF)模块,避免尺度特征的不一致性,提高检测精度;最后,将center-ness分支引入到回归分支中,引入联合交并比损失(GIoU loss, generalized intersection over union)提高检测的性能.实验数据集使用公开数据集Fish4Knowledge (F4K)中的图片以及视频帧截取图片,选取训练性能最优模型进行评估.结果表明,提出的新模型在以上数据集的平均检测精度分别为99.79%、99.88%,相较于原模型以及其他检测模型本文提出模型的检测精度与识别速度更高,可为海洋鱼类识别提供参考依据.

Improved FCOS Network for Marine Fish Target Detection

Exploring and protecting fish is an important part of maintaining the balance of the marine ecological environment. However, the complex underwater environment affected by light, water quality, and occlusions makes it difficult to identify blurred fish images captured underwater and consequently restricts the speed and accuracy of underwater fish target detection. To solve the above problem, this study proposes a marine fish identification model based on improved fully convolutional one-stage object detection (FCOS). Specifically, the model takes the one-stage FCOS algorithm as the basic structure and uses the lightweight MobileNetv2 as the backbone network, which not only ensures the detection accuracy but also improves the detection; then, an adaptive spatial feature fusion (ASFF) module is introduced to avoid the inconsistency in scale features and improve detection accuracy; finally, the center-ness branch is introduced into the regression branch, and the generalized intersection over union (GIoU) loss is introduced to improve detection performance. Regarding the experimental dataset, the pictures in the public dataset Fish4Knowledge (F4K) and video frame screenshots are utilized, and the model with the optimal training performance is selected for evaluation. The results show that the average detection accuracy of the proposed new model on the above datasets is 99.79% and 99.88%, respectively. Compared with the original model and other detection models, the proposed model provides higher detection accuracy and identification speed. The model in this study can provide a reference for marine fish identification.